Your AI Agent Passed Evals. That’s the Problem.
Passing evals doesn't mean your AI agent works — it means your tests missed how it fails. Why output-based evals create false confidence and what to measure instead.
Launch Week Day 5 (5/5): Generate Datasets from Your Data Sources
Your best evaluation data already exists — it's sitting in Google Drive, SharePoint, Notion, and S3. Dataset generation on Confident AI turns your existing documents into evaluation-ready datasets automatically.
Launch Week Day 4 (4/5): Auto-Categorize Traces & Threads
You can't improve what you can't see. Auto-categorization tells you what your users are actually asking, detects response drift, and shows you which categories perform best — and which ones need help.
Launch Week Day 3 (3/5): Auto-Ingest Traces into Datasets & Annotation Queues
Production traces are the best dataset you’ll ever get — but most teams never turn them into one. With auto-ingest, your traces flow straight into datasets and annotation queues, continuously.
Launch Week Day 2 (2/5): Scheduled Evals
Everyone agrees evals should run regularly. But nobody remembers to actually run them. Scheduled Evals fixes that — set the frequency, configure your mappings, and never scramble before a release again.
Announcing Launch Week Q1 '26! Day 1: Automated Error Analysis
Error analysis used to mean pulling traces in code, hacking together an LLM to recommend metrics, and hoping for the best. Confident AI now does it for you.
Multi-Turn LLM Evaluation in 2026: What You Need to Know
In this article, I'll break down multi-turn LLM evaluation — how it differs from single-turn, what metrics actually matter, and how to implement it.
The Step-By-Step Guide to MCP Evaluation
A step-by-step guide to MCP evaluation: how to test MCP-based LLM apps and agents, measure tool use and task completion, and catch failures with DeepEval.
AI Agent Evaluation: Metrics, Traces, Human Review, and Workflows
A practical guide to evaluating AI agents with LLM metrics and tracing—plus when human review matters, how it calibrates judges, and workflows that combine CI, sampling, and production signals.
LLM Arena-as-a-Judge: LLM-Evals for Comparison-Based Regression Testing
Everything you need to run LLM Arena-as-a-judge: a comparison-based, pairwise approach to regression testing LLMs, with DeepEval code to set it up.